docs: def contrast(image, mask = slice(None)):
      r"""
      Takes a simple or multi-spectral image and returns the contrast of the texture.
      
      Fcon = standard_deviation(gray_value) / (kurtosis(gray_value)**0.25)
      
      Parameters
      ----------
      image : array_like or list/tuple of array_like 
          A single image or a list/tuple of images (for multi-spectral case).
      mask : array_like
          A binary mask for the image or a slice object
      Returns
      -------
      contrast : float
          High differences in gray value distribution is represented in a high contrast value. 
          
      See Also
      --------
      
      
      """
      image = numpy.asarray(image)
      
      # set default mask or apply given mask
      if not type(mask) is slice:
          if not type(mask[0] is slice):
              mask = numpy.array(mask, copy=False, dtype = numpy.bool)
      image = image[mask]
      
      standard_deviation = numpy.std(image)
      kurtosis = stats.kurtosis(image, axis=None, bias=True, fisher=False)
      n = 0.25 # The value n=0.25 is recommended as the best for discriminating the textures.  
      
      Fcon = standard_deviation / (kurtosis**n) 
      
      return Fcon